RerrFact: Reduced Evidence Retrieval Representations for Scientific Claim Verification

Ashish Rana, Deepanshu Khanna, Tirthankar Ghosal, Muskaan Singh, Harpreet Singh, Prashant Singh Rana

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations

Abstract

Exponential growth in digital information outlets and the race to publish has made scientific misinformation more prevalent than ever. However, the task to fact-verify a given scientific claim is not straightforward even for researchers. Scientific claim verification requires in-depth knowledge and great labor from domain experts to substantiate supporting and refuting evidence from credible scientific sources. The SciFact dataset and corresponding task provide a benchmarking leaderboard to the community to develop automatic scientific claim verification systems via extracting and assimilating relevant evidence rationales from source abstracts. In this work, we propose a modular approach that sequentially carries out binary classification for every prediction subtask as in the SciFact leaderboard. Our simple classifier-based approach uses reduced abstract representations to retrieve relevant abstracts. These are further used to train the relevant rationale-selection model. Finally, we carry out two-step stance predictions that first differentiate non-relevant rationales and then identify supporting or refuting rationales for a given claim. Experimentally, our system RerrFact with no fine-tuning, simple design, and a fraction of model parameters fairs competitively on the leaderboard against large-scale, modular, and joint modeling approaches.

Original languageEnglish
JournalCEUR Workshop Proceedings
Volume3164
StatePublished - 2022
Externally publishedYes
Event2022 Workshop on Scientific Document Understanding, SDU 2022 - Virtual, Online
Duration: Mar 1 2022 → …

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